FY-4A/GIIRS Temperature Validation in Winter and Application in Cold Wave Monitoring

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  • Corresponding author: Xiang FANG, fangxiang@cma.gov.cn
  • Funds:

    Supported by the National Natural Science Foundation of China (42175014) and the National Key Research and Development Program of China (2021YFB3900400).

  • doi: 10.1007/s13351-022-2015-4
  • Note: This paper will appear in the forthcoming issue. It is not the finalized version yet. Please use with caution.

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  • In order to improve the operational application ability of the FY-4A new sounding dataset, in this paper, validation of the FY-4A Geosynchronous Interferometric Infrared Sounder (FY-4A /GIIRS) temperature was carried out using the balloon sounding temperature from meteorological sounding stations. More than 350,000 samples were obtained through time–space matching, and the results show that the FY-4A/GIIRS temperature mean bias (MB) is 0.07°C, the mean absolute error (MAE) is 1.80°C, the root-mean-square error (RMSE) is 2.546°C, and the correlation coefficient (RR) is 0.95. The FY-4A/GIIRS temperature error is relatively larger in the upper and lower troposphere, and relatively smaller in the middle troposphere; that is, the temperature at 500 hPa is better than that at 850 hPa. The temporal variation is smaller in the upper and middle troposphere than in the lower troposphere. The reconstruction of missing data of FY-4A/GIIRS temperature in cloudy areas is also carried out and the results are evaluated. The spatial distribution of reconstructed FY-4A/GIIRS temperature and ERA5 reanalysis data is consistent and completely retains the minimum temperature center with high precision of FY-4A/GIIRS. There are more detailed characteristics of intensity and position at the cold center than that of the reanalysis data. Therefore, an operational satellite retrieval temperature product with time–space continuity and high accuracy is formed. The reconstructed FY-4A/GIIRS temperature is used to monitor a strong cold wave event in November 2021. The results show that the product effectively monitors the movement and intensity of cold air activities, and it also has good indication for the phase transition of rain and snow triggered by cold wave.
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  • Fig. 1.  FY-4A/GIIRS temperature quality flag at 850 hPa (green: 00_perfect; blue: 01_good; red: bad) and FY-4A/AGRI infrared channel cloud image at 0000 UTC on November 7, 2021.

    Fig. 2.  Location (blue dot), number (blue number), and terrain height of meteorological sounding stations selected for data validation.

    Fig. 3.  The average matching distance between the 109 meteorological sounding stations and FY-4A/GIIRS temperature sounding points of the validation samples. (The horizontal coordinate is the number of meteorological sounding stations in Fig. 2.).

    Fig. 4.  Scatter distribution of FY-4A/GIIRS temperature and meteorological sounding station temperature.

    Fig. 5.  The average accuracy of FY-4A/GIIRS temperature in 10 vertical layers: (a) total number of matching samples; (b) correlation coefficient RR; (c) mean bias MB (red line), mean absolute error MAE (green line), and root-mean-square error RMSE (blue line); (d) MB (purple 00:00 and green 1200) and MAE (blue 0000 and red 1200).

    Fig. 6.  The average accuracy of FY-4A/GIIRS temperature in 10 vertical layers of 109 sounding stations (a: correlation coefficient RR; b: mean bias MB; c: mean absolute error MAE; d: root-mean-square error RMSE).

    Fig. 7.  FY-4A/GIIRS temperature accuracy verification at Beijing meteorological sounding station (station no. 54511, N71) (a: temperature at 850 hPa; b: bias at 850 hPa; c: temperature at 500 hPa; d: bias at 500 hPa; sample number N = 396 at 850 hPa, sample number N = 411 at 500 hPa).

    Fig. 8.  FY-4A/GIIRS temperature accuracy verification at Zhangjiakou meteorological sounding station (station no. 54401, N68) (a: temperature at 850 hPa; b: bias at 850 hPa; c: temperature at 500 hPa; d: bias at 500 hPa; sample number N = 444 at 850 hPa, sample number N = 448 at 500 hPa).

    Fig. 9.  FY-4A/GIIRS high-precision temperature (a), temperature after data reconstruction (b), ERA5 reanalysis temperature (c) at 850 hPa at 0000 on November 7, 2021, and the correlation coefficient spatial distribution of the two datasets in November 2021 (d).

    Fig. 10.  The 24-h temperature difference at 850 hPa at 0000 from November 4 to 8, 2021 (a: November 4–5; b: November 5–6; c: November 6–7; d: November 7–8).

    Fig. 11.  Vertical distribution of 24-h temperature difference at 850 hPa at 00:00 from November 6 to 7, 2021 (a: 105°E; b: 110°E; c: 115°E).

    Fig. 12.  At 0000, 0600, and 1200 on November 6 and 0000 on November 7, 2021, (a) FY-4A/GIIRS −4°C and 0°C temperature contour lines at 850 hPa, (b) ground observation snow line and sleet line, and ground observation weather symbol at 00:00 on November 7 ("*" is snow, "·" is rain and "*·" is sleet).

    Fig. 13.  The 6 and 12 h prediction of GRAPES-GFS model at 000, November 7, 2021, at 850 hPa: 6 h (a) and 12 h (b) forecasting temperature, 6 h (c) and 12 h (d) forecasting temperature difference between GRAPES-GFS model and FY-4A/GIIRS, and 24 h temperature change in 6 h (e) and 12 h (f) prediction.

    Table 1.  FY-4A/GIIRS temperature accuracy at Beijing meteorological sounding station (station no. 54511, N71)

    54511RRMBMAERMSEN
    100 hPa0.620.541.833.65460
    150 hPa0.61−0.181.443.34466
    200 hPa0.74−0.191.593.53476
    250 hPa0.760.131.563.50440
    300 hPa0.800.041.263.13414
    400 hPa0.890.921.442.55397
    500 hPa0.93−0.541.302.16411
    700 hPa0.950.801.612.24427
    850 hPa0.97−0.431.562.07396
    925h Pa0.96−1.061.932.36363
    average0.021.552.95
    Download: Download as CSV

    Table 2.  FY-4A/GIIRS temperature accuracy at Zhangjiakou meteorological sounding station (station no. 54401, N68)

    54401RRMBMAERMSEN
    100 hPa0.79−0.141.752.22484
    150 hPa0.850.221.201.56493
    200 hPa0.890.141.401.86499
    250 hPa0.93−0.101.251.60487
    300 hPa0.950.130.971.29442
    400 hPa0.971.111.301.59437
    500 hPa0.97−0.381.041.38448
    700 hPa0.960.901.612.19473
    850 hPa0.970.011.772.33444
    925 hPa0.920.082.603.28363
    average0.191.471.97
    Download: Download as CSV
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FY-4A/GIIRS Temperature Validation in Winter and Application in Cold Wave Monitoring

    Corresponding author: Xiang FANG, fangxiang@cma.gov.cn
  • 1. Key Laboratory of Radiometric Calibration and Validation for Environmental Satellites, Innovation Center for FengYun Meteorological Satellite (FYSIC), National Satellite Meteorological Center, China Meteorological Administration, Beijing 100081
  • 2. National Meteorological Center, China Meteorological Administration, Beijing 100081
Funds: Supported by the National Natural Science Foundation of China (42175014) and the National Key Research and Development Program of China (2021YFB3900400).

Abstract: In order to improve the operational application ability of the FY-4A new sounding dataset, in this paper, validation of the FY-4A Geosynchronous Interferometric Infrared Sounder (FY-4A /GIIRS) temperature was carried out using the balloon sounding temperature from meteorological sounding stations. More than 350,000 samples were obtained through time–space matching, and the results show that the FY-4A/GIIRS temperature mean bias (MB) is 0.07°C, the mean absolute error (MAE) is 1.80°C, the root-mean-square error (RMSE) is 2.546°C, and the correlation coefficient (RR) is 0.95. The FY-4A/GIIRS temperature error is relatively larger in the upper and lower troposphere, and relatively smaller in the middle troposphere; that is, the temperature at 500 hPa is better than that at 850 hPa. The temporal variation is smaller in the upper and middle troposphere than in the lower troposphere. The reconstruction of missing data of FY-4A/GIIRS temperature in cloudy areas is also carried out and the results are evaluated. The spatial distribution of reconstructed FY-4A/GIIRS temperature and ERA5 reanalysis data is consistent and completely retains the minimum temperature center with high precision of FY-4A/GIIRS. There are more detailed characteristics of intensity and position at the cold center than that of the reanalysis data. Therefore, an operational satellite retrieval temperature product with time–space continuity and high accuracy is formed. The reconstructed FY-4A/GIIRS temperature is used to monitor a strong cold wave event in November 2021. The results show that the product effectively monitors the movement and intensity of cold air activities, and it also has good indication for the phase transition of rain and snow triggered by cold wave.

    • The FY-4 satellite is the new generation of geostationary meteorological satellite after the FY-2 series in China (Yang et al., 2017). The scientific research satellite FY-4A was successfully launched in December 2016 with a three-axis stable attitude and carries a variety of observation instruments, including Advanced Geostationary Radiation Imager (AGRI), Geosynchronous Interferometric Infrared Sounder (GIIRS), geostationary orbit Lightning Mapping Imager (LMI), and a space environment monitoring instrument (Lu and Shou, 2011). Unlike other hyperspectral infrared (IR) sounders (Menzel et al., 2018), GIIRS is the first hyperspectral IR sounder onboard the geostationary satellite, which enables not only three-dimensional atmospheric temperature and humidity profiles (Zhang et al., 2017), but also three-dimensional horizontal wind field (Ma et al., 2021). FY-4A/GIIRS was developed by the Shanghai Institute of Technical Physics, Chinese Academy of Sciences. It uses Michelson interferometric spectroscopy to observe atmospheric infrared radiation. There are 1650 channels, including long-wave infrared (689) and medium-wave infrared (961) channels. It can provide the brightness temperature with long-wave infrared of 8.85–14.29 μm (wave number: 700–1130 cm−1) and short- and medium-wave infrared of 4.44–6.06 μm (wave number: 1650–2250 cm−1). The spectral resolution in wave number is 0.625 cm−1, and the spatial resolution is about 16 km near the satellite nadir (Zhang et al., 2016).

      The FY-4A/GIIRS three-dimensional temperature and humidity datasets of the atmosphere with high temporal resolution can make up for the shortcomings of conventional meteorological sounding data (ground meteorological sounding stations are about 200 km in spatial resolution and observe twice a day), and can also improve the initial field of the numerical predication model and thus improve the accuracy of the numerical prediction model and the refinement ability of weather prediction (Yin et al., 2019, 2021). At the same time, using the retrieved atmospheric instability index and other products, we can find the changes in environmental conditions before the occurrence of small` and medium-scale weather systems such as rainstorms and strong convection several hours in advance (Li et al., 2011), analyze the possible clues of extreme weather (Li et al., 2012), and directly serve the short-term and imminent weather forecast and early warning (Zhang et al., 2017).

      Cold waves are among the main disastrous weather events in winter in China. It brings strong wind, cooling, snowstorms, and freezing rain, which can cause serious losses to the national economy, life, and property (Deng, 1977; Liu, 1990). As early as 1955, Li (1955) divided the cold waves in East Asia into three types. In 1957, Tao (1957) classified the region of 45º–65ºN and 70º–90º E as the key area of cold waves, and studied the location and path of cold air sources affecting mainland China. In the early 1980s, the classical cold wave theory was formed through the study of the causes and prediction of cold waves and the energy analysis of the mid-term process of cold waves (Zhu et al., 1992). In the 21st century, the study of cold waves mainly focuses on the climate change of cold wave weather and its possible causes (Kang et al., 2010).

      The outbreak events of cold waves usually cause substantial cooling and windy weather in most parts of China. Previous studies on cold waves usually focus on the daily average temperature drop and the minimum air temperature anomaly during the cold wave process. Luo et al. (2019) used the accurate radiative transfer model LBLRTM to carry out spectral sensitivity analysis of infrared channels under different atmospheric conditions for FY-4A/GIIRS. The results show that the sensitivity of FY-4A/GIIRS long-wave infrared band temperature and humidity is related to atmospheric conditions. FY-4A/GIIRS is the most sensitive to tropical atmospheric disturbance, followed by mid-latitude in summer, and the sensitivity is the lowest in mid-latitude in winter. It has been revealed that FY-4A/GIIRS has the application potential of atmospheric temperature, humidity, and ozone profile. Under the same atmospheric conditions, the infrared spectrum of FY-4A/GIIRS contains the largest amount of temperature information, followed by water vapor. In addition, the precipitation in winter has phase variability. Whether it is rain, sleet, snow, freezing rain, or ice particles, precipitation will have a great impact on production and life. With the accelerating process of urbanization in China in recent years, the impact of meteorological disasters is becoming greater and greater. Especially, at the beginning of 2008, the low-temperature freezing rain and snow disaster caused serious losses to transportation, power transmission, and industrial and agricultural production in most parts of South China and had a serious impact on people's lives. The research on the identification criteria of the winter precipitation phase in Beijing shows that at 850 hPa, the temperature of rain and snow transformation is −4°C, and the temperature range of sleet is [−7°C, −2°C] (Zhang et al., 2013).

      Four cold wave processes occurred from October to November 2021. We try to use FY-4A/GIIRS temperature data with high temporal resolution to study the temperature changes and precipitation phase analysis of these cold wave processes. Therefore, the main research in this paper considers the following aspects. Firstly, according to the operational monitoring requirements of cold waves and the sounding coverage of FY-4A/GIIRS, the accuracy of temperature data in the winter of 2020–2021 is evaluated. Because FY-4A/GIIRS temperature is greatly affected by clouds, the temperature accuracy in the cloudy area is lower or missing, so in this paper, we also study the reconstruction method of the missing data. At last, using reconstructed FY-4A/GIIRS temperature data, the application effect of strong cold wave weather process in November 2021 is evaluated.

    2.   Data and methods
    • FY-4A/GIIRS is the first geostationary orbit interferometric vertical sensor. It adopts Michelson interference spectroscopy. By measuring the change in target radiation intensity with the moving distance of the moving mirror, the atmospheric infrared radiation spectrum can be obtained by Fourier transform. Since different spectral channels reflect the atmospheric radiation contribution at different heights, a group of appropriate spectral channels are selected. According to the principle of atmospheric infrared radiation transmission, the vertical distribution of parameters such as temperature can be obtained through numerical calculation (Zhang et al., 2016).

      FY-4A/GIIRS has 1650 spectral channels belonging to the long-wave infrared and medium-wave infrared bands, with a spectral resolution of 0.625 cm−1 wave number. The spatial resolution of the infrared channels is 16 km, and the observation region is (15º –55º N; 70º –140º E), covering China and its surrounding areas. The observation times are 00, 02, 04, 06, 08, 10, 12, 14, 20, and 22 (UTC) o'clock, for a total number of 10 times a day.

      Because the temperature profile retrieved by FY-4A/GIIRS is from the infrared band and the ability of infrared wave to penetrate the clouds is poor, the accuracy of the temperature is higher under clear sky, thin cloud conditions or above the cloud top height. When deep cloud system activities occur, the accuracy of the temperature in and under the clouds is relatively lower. The FY-4A/GIIRS temperature profile dataset also provides the data quality flag, according to the temperature. In this paper, we choose the data with high accuracy (quality flag is 0 or 1) for application research (Fig. 1), and the green (quality flag: 0) and blue (quality flag: 1) points marked in Fig. 1 are chosen in this paper.

      Figure 1.  FY-4A/GIIRS temperature quality flag at 850 hPa (green: 00_perfect; blue: 01_good; red: bad) and FY-4A/AGRI infrared channel cloud image at 0000 UTC on November 7, 2021.

      The data used in this paper include temperature and its quality flag. The temporal and spatial resolutions are the same as the observation of the instrument. There are 101 layers in the vertical direction. The pressure layers below 100 hPa are 103, 110, 118, 126, 134, 142, 151, 160, 170, 180, 190, 200, 212, 223, 235, 247, 260, 273, 286, 300, 314, 329, 344, 359, 375, 391, 407, 424, 442, 460, 478, 497, 516, 535, 555, 576, 596, 617, 639, 661, 685, 707, 730, 754, 778, 802, 827, 853, 879, 905, 932, 958 986, 1014, 1042, 1071, and 1100 hPa. The time periods of data used in this paper are from October to December 2020, January to March 2021, and October to November 2021.

    • Because the research focus of this paper is the application of FY-4A/GIIRS temperature in cold wave monitoring, the temperature of meteorological radiosonde in China and high latitudes which are covered by FY-4A/GIIRS are selected as the true values in the validation. The time periods of meteorological sounding data used are from October to December 2020, from January to March 2021, and from October to November 2021. There are two times each day, 0000 and 1200, and 11 layers in the vertical levels. Considering the influence of terrain height, a total of 10 layers are selected for validation in this paper, which are 100, 150, 200, 250, 300, 400, 500, 700, 850, and 925 hPa. (Zhi and Xu, 2013; Chen et al., 2017)

      The location, number (N01–N109), and terrain height of the radiosonde stations selected for validation are shown in Fig. 2, including 13 international meteorological radiosonde stations (9 in Russia, 3 in Mongolia and 1 in Kazakhstan) and 96 Chinese meteorological radiosonde stations, for a total of 109. Most of the selected stations have low altitude and can effectively detect the temperature at 850 hPa.

      Figure 2.  Location (blue dot), number (blue number), and terrain height of meteorological sounding stations selected for data validation.

    • The meteorological reanalysis data are used to evaluate the reconstruction effect of FY-4A/GIIRS temperature in the cloudy area where the data are missing. The reanalysis dataset ERA5 temperature from the European Centre for Medium-Range Weather Forecasts (ECWMF) (Hersbach et al., 2020) is selected. The reanalysis dataset is a fusion of model data and global observation data; the unit of the temperature data is K; the horizontal resolution is 0.25°, which is divided into 37 vertical layers; 500 and 850 hPa pressure layers are selected; and the time resolution is 1 h.

    • With the requirements of the continuity of weather process and the accuracy and beauty of images in meteorological services, the FY-4A/GIIRS temperature field in cloudy areas or missing data areas must be reconstructed to form a spatially continuous and uniform temperature field. At present, there are many kinds of data interpolation algorithms (Zhang et al., 2009; Liu et al., 2020 本条文献在文后文献中未体现), such as Kriging interpolation method, inverse distance weighted method (IDW), spline interpolation method, and Cressman interpolation method. Cressman objective analysis method is a gradually revised interpolation method that interpolates discrete points into regular grid points, bringing less error. It is widely used in various diagnostic analyses in the field of meteorology. Based on the analysis of common interpolation algorithms, in this paper, the method of step-by-step interpolation analysis is adopted to optimize the Cressman interpolation algorithm and realize improvements in calculation speed and high-precision iteration. See formulas (1) and (2) for Cressman interpolation:

      $$ \alpha ' = {\alpha _0} + \Delta {\alpha _{i,j}}. $$ (1)
      $$ \Delta {\alpha _{i,j}} = \dfrac{{ \displaystyle\sum\nolimits_{k = 1}^K \left( {W_{i,j,k}^2\Delta {\alpha _k}} \right)}}{{ \displaystyle\sum\nolimits_{k = 1}^K {W_{i,j,k}}}}. $$ (2)

      where $\alpha $ is the meteorological variable (temperature T in this paper), ${\alpha _0}$ is the first guess value of the variable $\alpha $ on the grid point $(i,j)$, the first guess value selected in this paper is 0 (${\alpha _0} = 0$), $\alpha '$ is the revised value of the variable $\alpha $ on the grid point $(i,j)$, $ \Delta {\alpha _k}$is the difference between the observed value of the observation point k and the first guess value (in this paper, ${\alpha _0} = 0$; $ \Delta {\alpha _k}$ is the observed value of observation point k), and ${W_{i,j,k}}$ is the weight function. The weight function is determined by formula (3):

      $$ {W_{i,j,k}}{\text{ = }}\left\{ \begin{gathered} \frac{{{R^2} - d_{i,j,k}^2}}{{{R^2} + d_{i,j,k}^2}}({d_{i,j,k}} < R) \hfill \\ 0({d_{i,j,k}} \geqslant R) \hfill \\ \end{gathered} \right.. $$ (3)

      where R is the influence radius. In this paper, according to the maximum spatial resolution of FY-4A/GIIRS temperature product of 16 km, R = 30 km is selected. ${d_{i,j,k}}$is the distance from the grid point $(i,j)$ to the observation point k. The calculation method is formula (4):

      $$\begin{aligned}[b] {d_{i,j,k}} & = r \times {\text{acos[cos(}}\frac{{{\text{lat}}{1_k} \times \pi }}{{180}}) \\ & \times {\text{cos(}}\frac{{{\text{lat}}{2_{i,j}} \times \pi }}{{180}}) \times \cos (\frac{{{\text{lon}}{1_k} \times \pi }}{{180}} - \frac{{{\text{lon}}{2_{i,j}} \times \pi }}{{180}}) \\ & + \sin (\frac{{{\text{lon}}{1_k} \times \pi }}{{180}}) \times \sin (\frac{{{\text{lon}}{2_{i,j}} \times \pi }}{{180}})] \end{aligned}$$ (4)

      where $lon{1_k}$and $lat{1_k}$ are the longitude and latitude of the observation point k, respectively; ${\text{lon}}{{\text{2}}_{i,j}}$and ${\text{lat}}{{\text{2}}_{i,j}}$are the longitude and latitude of the grid point $(i,j)$, respectively; and $ r = 6371{\text{km}} $,which is the Earth’s radius, $ \pi = 3.1415 $.

      In this paper, the spatial resolution of the grid interpolation is 0.1º by latitude and longitude. FY-4A/GIIRS temperature data include about 1628 × 32 observation points for one single level at each time (101 levels in total). In order to improve the calculation speed, except for the first interpolation, the observed values of subsequent iterative interpolation algorithms are the results of the previous grid interpolation. We searched and calculated the value less than or equal to the influence radius in the range of 10 grid points for multiple interpolation iterations, greatly improving the calculation efficiency.

    • According to the horizontal maximum spatial resolution of FY-4A/GIIRS sounding of 16 km, and considering that the spatial resolution is lower than 16 km in high latitude, we take the selected meteorological sounding station location as the center and search the nearest FY-4A/GIIRS sounding point within 50 km for horizontal spatial matching. The distribution of the nearest distance between 72 meteorological sounding stations and FY-4A/GIIRS sounding matching is shown in Fig. 3; the matching distance affects the accuracy of data verification to a certain extent. The shorter the matching distance is, the more objective the data accuracy verification.

      Figure 3.  The average matching distance between the 109 meteorological sounding stations and FY-4A/GIIRS temperature sounding points of the validation samples. (The horizontal coordinate is the number of meteorological sounding stations in Fig. 2.).

      As can be seen from Fig. 3, except for the matching distances of N03 (29862, 28.42 km in Russia), N14 (51076, 23.86 km in Xinjiang), N18 (51709, 21.24 km in Xinjiang), and N60 (50953, 23.52 km in Heilongjiang), which are more than 20 km, the matching distances of the other 105 stations are close to or less than 16 km, which is the spatial resolution of FY-4A/GIIRS temperature data. Among them, the number of the Beijing meteorological station (56511) is N71, and the average matching distance is 16.00 km. The number of the Zhangjiakou meteorological station (54401) in Hebei province is N68, and the average matching distance is 7.49 km, which is equal to or less than the horizontal spatial resolution of FY-4A/GIIRS sounding.

    • According to the vertical layer distribution of meteorological sounding stations, 100, 150, 200, 250, 300, 400, 500, 700, 850, and 925 hPa are selected for vertical layer matching. The vertical layers of FY-4A/GIIRS temperature data are 103, 151, 200, 247, 300, 407, 497, 707, 853, and 932 hpa, respectively. The difference between two data points in matching vertical layers will also give objectivity in data verification.

    • The FY-4A/GIIRS temperature at 0000 and 1200 is matched with that of the meteorological sounding station temperature at 0000 and 1200.

    • In FY-4A/GIIRS temperature error analysis, the bias ($ {B_i} $), mean bias (MB), error ($ E $), mean absolute error (MAE), root-mean-square error (RMSE), and correlation coefficient (RR) are mainly considered. The calculation methods are formulas (5), (6), (7), and (8):

      $$ {\text{MB}} = \frac{1}{N}\sum\nolimits_{i = 1}^N {{B_i}} {B_i} = ({E_i} - {O_i}) . $$ (5)
      $$ {\text{MAE}} = \frac{1}{N}\sum\nolimits_{i = 1}^N {{E_i}} {E_i} = |{E_i} - {O_i}| . $$ (6)
      $$ {\text{RMSE}} = \sqrt {\frac{1}{N}\sum\nolimits_{i = 1}^N {{{(E{}_i - O{}_i)}^2}} } . $$ (7)
      $$ {\text{RR}} = \dfrac{{\displaystyle\sum\nolimits_{i = 1}^N {[(E{}_i - \bar E)(O{}_i - \bar O)]} }}{{\sqrt {\displaystyle\sum\nolimits_{i = 1}^N {{{(E{}_i - \bar E)}^2}} } \sqrt {\displaystyle\sum\nolimits_{i = 1}^N {{{(O{}_i - \bar O)}^2}} } }} . $$ (8)

      where $ N $ is the number of samples, $ E{}_i $is the FY-4A/GIIRS temperature of sample i, $ O{}_i $is the sounding temperature of sample i, $ \bar E $ is the average FY-4A/GIIRS temperature of the $ N $samples, and $ \bar O $ is the average sounding temperature of the $ N $samples.

    3.   Research results
    • In the period studied in this paper, through time and space matching methods for 109 sounding stations at 10 vertical levels for two kinds of datasets, the number of matching samples is $ N = 357097 $. On average (Fig. 4), FY-4A/GIIRS temperature mean bias: $ \text{MB}=0.07°C $, mean absolute error: $ \text{MAE}=1.80°C $, root-mean-square error: $ \text{RMSE}=2.46°C $, and correlation coefficient: $ {\text{RR}} = 0.95 $. From −50°C to −20°C (at middle troposphere), the scatter distribution is closer to the linear regression line, and the FY-4A/GIIRS temperature accuracy is higher and more stable.

      Figure 4.  Scatter distribution of FY-4A/GIIRS temperature and meteorological sounding station temperature.

      The number of matching samples N of 10 vertical layers is shown in Fig. 5a. It is the most at 150 hpa (43692) and the least at 925 hPa (21575), and below 850 hPa, the number significantly decreases. The vertical distribution characteristics of the number of matching samples are related to the influence of clouds. When there are clouds with low cloud top height in the observation area, the temperature accuracy is less affected at the level above the cloud top height, and the temperature accuracy is low or the data are missing at the level with clouds or under the clouds.

      Figure 5.  The average accuracy of FY-4A/GIIRS temperature in 10 vertical layers: (a) total number of matching samples; (b) correlation coefficient RR; (c) mean bias MB (red line), mean absolute error MAE (green line), and root-mean-square error RMSE (blue line); (d) MB (purple 00:00 and green 1200) and MAE (blue 0000 and red 1200).

      As can be seen from the distribution of correlation coefficients RR at different levels (Fig. 5b), the maximum coefficient is 0.98 at 300, 400, and 500 hPa in the middle troposphere, and the correlation coefficient at 200 hPa is relatively low, 0.86. The layers with large mean bias MB are 100 hPa (1.36°C), 925 hPa (−0.91°C), 400 hPa (0.76), and 850 hPa (−0.62°C) (Fig. 5c), which are generally positive in the upper troposphere and negative in the lower troposphere. The distributions of mean absolute error MAE and root-mean-square error RMSE are relatively larger in the upper and lower troposphere, and relatively smaller in the middle troposphere. The minimum mean absolute error MAE is 1.33°C, and the minimum root-mean-square error RMSE is 1.85°C, which are located in the middle troposphere. The maximum mean absolute error MAE is 2.44°C, and the maximum root-mean-square error RMSE is 3.36°C, which are located at 925 hPa and 100 hPa, respectively. The mean absolute error MAE and root-mean-square error RMSE at 850 hPa are 2.27°C and 2.99°C, respectively. The relatively large error at 100 hPa in the upper troposphere may be related to the spatial matching distance of the meteorological sounding stations. When the sounding balloon rises from the ground to the upper air, the higher the height from the ground, the greater the distance from the sounding station due to the influence of the wind field. In the data verification, this may cause a greater matching horizontal distance between FY-4A/GIIRS temperature and radiosonde observation temperature. The gradually increasing error below 700 hPa may be related to the influence of clouds. It can also be seen from Fig. 5a that the number of matching samples below 700 hPa is also significantly less, and the reduction in matching samples is mainly affected by clouds.

      Yin et al. (2020) showed that FY-4A/GIIRS has temporal variation, where the diurnal variation in biases is obvious only for the upper tropospheric channels, and the biases for high tropospheric channels are smaller than the biases for low tropospheric channels. The FY-4A/GIIRS temperature mean bias and mean absolute error show the same characteristics in vertical distribution by comparing the accuracy at 0000 and 1200 (Fig. 5d), showing relatively poor data quality at 100 hPa and at lower than 800 hPa at 1200. In the upper and mid-troposphere, the temporal variation is small.

    • Fig. 6 shows the accuracy in 10 layers of 107 meteorological sounding stations. The accuracy difference of 107 stations at 250, 300, 400, 500, and 700 hPa layers is small. The temperature correlation coefficient for most stations is more than 0.9, the mean bias is -1−1°C, the mean absolute error is 1–2°C, and the root-mean-square error is about 1–2.5°C. At these barometric layers, the stations with relatively low correlation coefficients and large errors are N02, N23, N63, and N100–N109 (Fig. 1). The temperature accuracy of FY-4A/GIIRS varies greatly at different radiosonde stations at 100, 850, and 925h Pa pressure layers. Stations with large errors are N01–N26 (mainly in Russia and Xinjiang) and N56–N64 (in Northeast China). The FY-4A/GIIRS temperatures in the regions where N42–N53 and N65–N109 stations are located (in Inner Mongolia, North China, the east of Northwest China, and South China) have high accuracy at most vertical levels.

      Figure 6.  The average accuracy of FY-4A/GIIRS temperature in 10 vertical layers of 109 sounding stations (a: correlation coefficient RR; b: mean bias MB; c: mean absolute error MAE; d: root-mean-square error RMSE).

    • In order to obtain more detailed information on FY-4A/GIIRS temperature accuracy, according to the meteorological operational service for winter cold waves in China, Beijing (station no. 54511, N71) and Zhangjiakou (station no. 54401, N68) meteorological sounding stations are selected for data accuracy verification. Tables 1 and 2 show the correlation coefficient $ RR $, mean bias MB, mean absolute error MAE, root-mean-square error RMSE, and the number of samples$ N $of the 10 pressure layers for the two radiosonde stations.

      54511RRMBMAERMSEN
      100 hPa0.620.541.833.65460
      150 hPa0.61−0.181.443.34466
      200 hPa0.74−0.191.593.53476
      250 hPa0.760.131.563.50440
      300 hPa0.800.041.263.13414
      400 hPa0.890.921.442.55397
      500 hPa0.93−0.541.302.16411
      700 hPa0.950.801.612.24427
      850 hPa0.97−0.431.562.07396
      925h Pa0.96−1.061.932.36363
      average0.021.552.95

      Table 1.  FY-4A/GIIRS temperature accuracy at Beijing meteorological sounding station (station no. 54511, N71)

      54401RRMBMAERMSEN
      100 hPa0.79−0.141.752.22484
      150 hPa0.850.221.201.56493
      200 hPa0.890.141.401.86499
      250 hPa0.93−0.101.251.60487
      300 hPa0.950.130.971.29442
      400 hPa0.971.111.301.59437
      500 hPa0.97−0.381.041.38448
      700 hPa0.960.901.612.19473
      850 hPa0.970.011.772.33444
      925 hPa0.920.082.603.28363
      average0.191.471.97

      Table 2.  FY-4A/GIIRS temperature accuracy at Zhangjiakou meteorological sounding station (station no. 54401, N68)

      The horizontal spatial matching distance of the Beijing meteorological sounding station is about 16.07 km (Fig. 3), and the number of matching samples is about 350–450 (Table 1). The correlation coefficient of the two datasets is between 0.62 and 0.96; the correlation coefficient is slightly smaller at the upper troposphere and increases gradually from the middle to lower troposphere. The 10 layers’ average MB=0.02°C, MAE=1.55°C, and RMSE}=2.95°C. At 850 hPa in the lower troposphere, which is very impotent in cold wave monitoring in winter, RR=0.97, MB=−0.43°C, MAE=1.56°C, and RMSE=2.07°C. At 500 hPa in the middle troposphere, RR=0.93, MB=−0.54°C, MAE=1.30°C, and RMSE=2.16°C.

      The horizontal spatial matching distance of the Zhangjiakou meteorological sounding station in Hebei Province is about 7.49 km (Fig. 3), and the number of matching samples is slightly larger than that of the Beijing meteorological station, which is about 350–500 (Table 2). The correlation coefficient of the two data is between 0.79 and 0.97, which also shows that the high-level correlation coefficient is slightly smaller, and the correlation coefficient below 250 hPa is greater than 0.92. On the whole, the data correlation coefficient is slightly larger than that of the Beijing meteorological station. The 10 layers' average MB=0.19°C,MAE=1.47°C, and RMSE=1.97°C. At 850 hPa in the lower troposphere, RR=0.97, MB=0.01°C,MAE=1.77°C, and RMSE=2.33°C. At 500 hPa in the mid-troposphere, RR=0.93,MB=−0.38°C,MAE=1.04°C,and RMSE=1.38°C. Overall, the FY-4A/GIIRS temperature accuracy at the Zhangjiakou meteorological sounding station in Hebei province is slightly better than that of the Beijing meteorological sounding station.

      It can be seen from the comparison of temperature and bias of all matching samples of the two datasets one by one (Fig. 7 and Fig. 8) that the trend of FY-4A/GIIRS temperature at 850 hPa (Fig. 7a) at the Beijing meteorological sounding station is consistent with sounding temperature. Except for a few cases, the warming and cooling processes of the 396 matching samples agree with each other. There are more negative bias samples (Fig. 7b). The total number of samples with absolute error greater than 4°C (bias greater than 4°C and less than −4°C) is 20, the number of samples with absolute error of 3–4°C is 26, and the number of samples with absolute error greater than 3°C accounts for about 11.6% of the total number of samples (369). The FY-4A/GIIRS temperature mean absolute error at 500 hPa (1.29°C) is smaller than that at 850 hPa (1.56°C) (Table 1) (Figs. 7c,d), and the consistency of data accuracy is better. The number of samples with absolute error greater than 4°C is 12, the number of samples with absolute error of 3–4°C is 13, and the number of samples with absolute error greater than 3°C accounts for about 6.8% of the total number of samples (411).

      Figure 7.  FY-4A/GIIRS temperature accuracy verification at Beijing meteorological sounding station (station no. 54511, N71) (a: temperature at 850 hPa; b: bias at 850 hPa; c: temperature at 500 hPa; d: bias at 500 hPa; sample number N = 396 at 850 hPa, sample number N = 411 at 500 hPa).

      Figure 8.  FY-4A/GIIRS temperature accuracy verification at Zhangjiakou meteorological sounding station (station no. 54401, N68) (a: temperature at 850 hPa; b: bias at 850 hPa; c: temperature at 500 hPa; d: bias at 500 hPa; sample number N = 444 at 850 hPa, sample number N = 448 at 500 hPa).

      The trend of FY-4A/GIIRS temperature at 850 hPa at the Zhangjiakou meteorological sounding station (Fig. 8a) is also in agreement with the sounding temperature. The total number of samples with absolute error greater than 4°C (Fig. 8b) is 23, the number of samples with absolute error of 3–4°C is 36, and the number of samples with absolute error greater than 3°C accounts for about 13.3% of the total number of samples (444). The proportion of samples with error greater than 3°C is slightly higher than that of the Beijing meteorological sounding station. The FY-4A/GIIRS temperature mean average error (1.04°C) at 500 hPa is better than that at 850 hPa (1.77°C) (Table 1) (Figs. 8c,d). Similarly, the consistency of data accuracy is also better. The number of samples with absolute error greater than 4°C is four, the number of samples with absolute error of 3–4°C is ten, and the number of samples with absolute error greater than 3°C accounts for about 3.1% of the total number of samples (448), which is better than that of the Beijing meteorological sounding station at 500 hPa.

    • FY-4A/GIIRS has less effective sounding in cloudy areas, and the quality of most data is identified as poor (red points in Fig. 1). In order to achieve a better application effect, it is necessary to remove data with poor accuracy and only select the data with high accuracy (green and blue points in Fig. 1). Due to the influence of cloud and quality identification control, there sometimes will be a large range of missing data in some areas (Fig. 9a). The missing data must be reconstructed to meet the operational service requirements for the continuity of weather system monitoring. In this paper, the data reconstruction method described in Section 2.4 is adopted.

      Figure 9.  FY-4A/GIIRS high-precision temperature (a), temperature after data reconstruction (b), ERA5 reanalysis temperature (c) at 850 hPa at 0000 on November 7, 2021, and the correlation coefficient spatial distribution of the two datasets in November 2021 (d).

      As shown in Fig. 9a,b, the FY-4A/GIIRS temperature distribution at 850 hPa is compared before and after the data reconstruction. Following the optimized interpolation algorithm, the missing data are filled in the cloud-affected regions of Huanghuai, North China, Northeast China, central and eastern Inner Mongolia, and northwestern Mongolia, which were the key regions of cold air activity during the cold wave event on November 7, 2021. The comparison with the ERA5 reanalysis data at the same time (Fig. 9c) shows that the reconstructed FY-4A/GIIRS temperature in the regions of missing data is consistent with that of the reanalysis data. It can be seen from the contour line of −4°C in Figs. 9b,c that they are all located in central Jilin, west of Liaoning, southeast of Hebei, west of Henan, west of Hubei, south of Shanxi province, and south of Gansu province. Research has shown that the −4°C temperature contour line at 850 hPa is the phase transition temperature of rain and snow in Beijing (Zhang et al., 2013). At the same time, FY-4A/GIIRS temperature reconstruction completely retains the cold center of the high-precision and effective sounding area (Figs. 9a,b). The intensity and location of the cold center in central Inner Mongolia and central and eastern Mongolia have more fine features than that of the ERA5 reanalysis data (Fig. 9c).

      Fig. 9d shows the spatial distribution of FY-4A/GIIRS reconstructed temperature and ERA5 temperature correlation coefficients at 850 hPa. At 0000 on November 1–30, 2021, the data show that except for the Tibetan Plateau, the correlation coefficient in most regions of China is greater than 0.8, and the correlation coefficient in Mongolia, the east of Northwest China, and North China is greater than 0.9 in the key areas of cold air activity. It shows that the reconstructed temperature has efficient performance in cold wave monitoring.

    • From November 4 to 8, 2021, a strong cold wave weather event occurred in China, with strong cooling and wide influence. The cold air activity caused a sharp drop in temperature. The daily minimum temperature in most areas of China decreased by 10–14°C, and even up to 16°C in some areas. The daily minimum temperature at many national meteorological observation stations reached or exceeded the extreme historical records in early November. It also brought a wide range of rain and snow. The daily precipitation in many places exceeded the extreme historical values. The daily snowfall in eastern and northeastern Inner Mongolia was characterized by heavy snow and extra heavy snow. FY-4A/GIIRS temperature after data reconstruction is used to monitor this strong cold wave process.

      It can be seen from the 24-h temperature difference at 0000 (Fig. 10) from November 4 to 5 that the cold air affected most of Mongolia, Xinjiang, and Inner Mongolia, and the maximum cooling center exceeded 14°C. From November 5 to 6, the cold air pushed south-eastward, and it moved faster in the western part (Gansu province). The strong temperature decrease appeared in Gansu, Ningxia, Inner Mongolia, and west of Heilongjiang province. From November 6 to 7, the cold air reached the east of Southwest China, the east of Northwest China, North China, and Huanghuai and Jianghan regions. From November 7 to 8, the cold air continued to move south-eastward, influencing the Huanghuai, Jianghuai, and Jiangnan regions. The maximum 24-h cooling was more than 14°C, even up to 16°C in some areas. Fig. 11 shows the vertical profile of the 24-h temperature difference from November 6 at 0000 to 7 at 0000 along the east longitudes 105º, 110º, and 115ºE. It can be seen that during this cold wave process, the cold air mass was deep, reaching up to 350 hPa. The cold air affected the lower troposphere earlier than the middle layer, and the temperature drop intensity in the lower troposphere was stronger.

      Figure 10.  The 24-h temperature difference at 850 hPa at 0000 from November 4 to 8, 2021 (a: November 4–5; b: November 5–6; c: November 6–7; d: November 7–8).

      Figure 11.  Vertical distribution of 24-h temperature difference at 850 hPa at 00:00 from November 6 to 7, 2021 (a: 105°E; b: 110°E; c: 115°E).

      Under the influence of cold air and warm air with high humidity, rainy and snowy weather occurred in Inner Mongolia, Northeast China, North China, the Huanghuai region, and east of Northwest China from November 6 to 7, and rainstorms or heavy rainstorms occurred in some areas. As can be seen from the evolution of the −4°C FY-4A/GIIRS temperature contour line at 850 hPa (Fig. 12a), at 0000 on the 6th, the −4°C temperature contour line was located in the west of Northeast China, the southeast and middle of Inner Mongolia, the north of Shanxi province, and the south of Gansu province, which is almost consistent with the snow line monitored by the ground meteorological observation stations (Fig. 12b). At 0600 on the 6th, the −4°C temperature contour line was slightly extended southward, and it began to snow in the north and northeast of Hebei province. At 2000 on the 6th, snowfall occurred successively in Beijing, north-central Hebei province, and the north of Shanxi province, and the −4°C temperature contour line was consistent with the snow line observed on the ground. At 0000 on the 7th (Fig. 9b and 12), the −4°C temperature contour line began to push southward to the south of Hebei province and west of Henan province, and there was sleet in the areas with −4 to 0°C temperature (blue and yellow contour lines in Fig. 12). FY-4A/GIIRS temperature effectively monitored the transformation of rain and snow phases during this cold wave. Generally speaking, at 850 hPa, the −4°C temperature contour line can be used as the key indicator of the snow line, −4 to 0°C is for sleet, and above 0°C is for rain.

      Figure 12.  At 0000, 0600, and 1200 on November 6 and 0000 on November 7, 2021, (a) FY-4A/GIIRS −4°C and 0°C temperature contour lines at 850 hPa, (b) ground observation snow line and sleet line, and ground observation weather symbol at 00:00 on November 7 ("*" is snow, "·" is rain and "*·" is sleet).

    • Another application of FY-4A/GIIRS temperature in cold wave monitoring is the accuracy verification of model prediction. The verification of the GRAPES-GFS model of 6 h and 12 h predicted temperature at 850 hPa (Fig. 13) shows that, compared with the FY-4A/GIIRS data (Fig. 9b), the 6 h and 12 h predicted temperatures in Mongolia, the east of Northwest China, and Inner Mongolia are 1–4°C higher, especially in Mongolia, where the temperature difference is more than 5°C. At the beginning of the cold air advancing, the difference between the predicted temperature and FY-4A observed temperature is relatively small in North China, Huanghuai, and the east of Southwest China, and the distributions of −4 and 0°C contour lines are very similar, indicating that the model has a good prediction for cold air advancing, while the predicted temperature deviated near the cold air center. From the predicted 24-hour temperature change, the maximum cooling center is located in the central and western part of Inner Mongolia, north and west of the cooling center monitored by FY-4A (Fig. 10c), and the range of temperature decrease at the beginning of the cold air advancing is lower.

      Figure 13.  The 6 and 12 h prediction of GRAPES-GFS model at 000, November 7, 2021, at 850 hPa: 6 h (a) and 12 h (b) forecasting temperature, 6 h (c) and 12 h (d) forecasting temperature difference between GRAPES-GFS model and FY-4A/GIIRS, and 24 h temperature change in 6 h (e) and 12 h (f) prediction.

    4.   Conclusions and discussion
    • In order to improve the operational application ability of the FY-4A new sounding data, we analyzed the accuracy of FY-4A/GIIRS temperature in winter by statistical analysis. We studied the data reconstruction method of temperature in the area of missing data affected by clouds, and evaluated the results using reanalysis data. At last, the application was carried out for the cold wave in November 2021. The main conclusions are as follows:

      (1) In the accuracy verification of FY-4A/GIIRS temperature data, the observational temperatures from 109 meteorological sounding stations are selected as the true values. Through time and space matching, there are 357,097 samples. On average, the mean bias of FY-4A/GIIRS temperature is MB=0.07°C, mean absolute error is MAE=1.80°C, root-mean-square error is RMSE=2.46°C, and correlation coefficient is RR=0.95. In the temperature range of −50°C to −20°C (middle troposphere), the scatter distribution is closer to the linear regression line, and the data accuracy is higher and more stable.

      (2) The accuracy verification of FY-4A/GIIRS temperature in different vertical layers shows that the maximum correlation coefficient is 0.98 at 300, 400, and 500 hPa (the middle troposphere), and the relatively low correlation coefficient is 0.86 at 200 hPa. The layers with large mean bias are 100 hPa (1.36°C), 925 hPa (−0.91°C), and 400 hPa (0.76°C), which are generally positive in the upper troposphere and negative in the lower troposphere. The distributions of mean absolute error and root-mean-square error are relatively larger in the upper and lower troposphere, and relatively smaller in the middle troposphere. The minimum mean absolute error is 1.33°C, and the minimum root-mean-square error is 1.85°C, which are all located in the middle troposphere. The maximum mean absolute error is 2.44°C, and the maximum root-mean-square error is 3.36°C, which are located at 925 hPa and 100 hPa, respectively. The mean absolute error and root-mean-square error at 850 hPa are 2.27°C and 2.99°C, respectively.

      (3) The accuracy verification of FY-4A/GIIRS temperature at different meteorological sounding stations shows that the stations with large errors are mainly concentrated in Russia, Xinjiang, and Northeast China. The FY-4A/GIIRS temperatures in Inner Mongolia, North China, the Huanghuai region, and the east of Northwest China have high accuracy at all pressure levels. The temperature accuracy verification at the Beijing and Zhangjiakou meteorological sounding stations, which are the key areas of cold wave monitoring, show that at the Beijing station, MB=0.02°C, MAE=1.55°C, and RMSE=2.95°C, and at the Zhangjiakou station, MB=0.19°C, MAE=1.47°C, and RMSE=1.97°C, showing high data quality. On the whole, the FY-4A/GIIRS temperature data quality at the Zhangjiakou station is slightly better than that of Beijing, and can meet the needs of cold wave monitoring.

      (4) The accuracy of FY-4A/GIIRS temperature is greatly affected by the clouds. In order to obtain better application effects of cold wave monitoring, it is necessary to remove data with poor accuracy, and there may be a large range of missing data in some areas. The data must be reconstructed to meet the operational service demand for the continuity of weather system monitoring. The improved Cressman interpolation algorithm was used to reconstruct the temperature. The effect evaluation shows that the reconstructed temperature is almost consistent with the reanalysis data. The data reconstruction completely retains the cold center of the high-precision and effective sounding, and the strength and position of the cold center have more fine features than that of ERA5 reanalysis data. The spatial distribution of FY-4A/GIIRS reconstructed temperature and ERA5 temperature correlation coefficients at 850 hPa in most regions of China are greater than 0.8.

      (5) The FY-4A/GIIRS temperature after reconstruction was used to monitor the cold wave process from November 4 to 8, 2021. The results show that the cold wave process has deep cold air mass, the cold air affected the lower troposphere earlier than the middle troposphere, the temperature drop intensity in the lower troposphere is stronger, and the strongest 24-h temperature difference is more than 16°C in some areas. FY-4A/GIIRS temperature adequately monitored the phase transformation of rain and snow during this cold wave. At 850 hPa, the −4°C temperature contour line can be used as the index of the snow line, −4 to 0°C is for sleet, and above 0°C is for rain.

      In the data accuracy verification of FY-4A/GIIRS, the results are affected by many factors. One is the selection of the truth value in this paper. The meteorological sounding station data are considered to constitute one of the best observation datasets close to the actual atmosphere temperature. However, the influence of wind will cause the sounding balloon to deviate from the meteorological sounding station as it reaches the tropopause from the ground. The greater the wind speed and the higher the sounding balloon, the greater the influence of position deviation. Another factor is the difference in spatial matching distance between the two types of data. When the spatial matching distance is smaller, the accuracy verification is closer to the actual error. In this paper, when selecting the maximum matching distance, we also must consider the number of samples that can be matched. The smaller the maximum matching distance, the fewer matching samples, and the more matching samples, the closer to the real error.

      When there is a large area of missing data affected by clouds, the reconstruction effect of FY-4A/GIIRS temperature in the missing data areas is poor. Therefore, it is suggested to study the fusion application of FY-4A/GIIRS temperature and microwave temperature from FY-3 satellites, giving play to the advantages of FY-4A/GIIRS temperature product in high-time resolution and high-precision sounding in cloudless or less cloudy areas, and the penetration of polar orbiting meteorological satellite microwave sounding in cloudy areas, so as to achieve better monitoring application services.

      The FY-4A/GIIRS temperature accuracy verification carried out in winter shows that the data have high quality in clear sky and thin cloud areas and obvious temporal variations in the lower troposphere. At present, the application and research on FY-4A/GIIRS temperature data are still insufficient. The research on this tool in atmospheric heating, cold air activity, and atmospheric instability related to strong convection activities in summer will give full play to the precision monitoring capability of FY-4A satellites. Particularly, the application research of these data over the Tibetan Plateau, oceans, and other regions will effectively make up for the lack of radiosonde observation.

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